FrameNet stores lexical data into an XML database. We pulled out the semantics
of FrameNet and its data by using Semion~\cite{Semion}, a tool grounded on
a method with two main steps: (i) a syntactic
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and completely automatic
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transformation of the data source to RDF
datasets according to an OWL ontology that represents the data source structure,
i.e. the source meta-model, (ii) a semantic refactoring that
allows to express the RDF triples according to specific domain ontologies e.g.
SKOS, DOLCE, FOAF, the Gene Ontology, or anything indicated by the user. This last action results in a RDF dataset, which
expresses the knowledge stored in the original data source, according to a set of assumptions on the domain
semantics, as selected and customized by the user. 
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The refactoring step is the result of a non-trivial knowledge engineering work
that requires a good knowledge of the target domain semantics.
For that reason the refactoring is semi-automatic as it requires the design of
transformation rules by the user.
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More exhaustively,
the refactoring is performed by means of transformation rules of the
form ``$condition \rightarrow consequent$'' whose aim is to apply a
tansformation (specified in the consequent) in the RDF graph only if the
condition is satisfied with respect to the knowledge expressed in the source RDF
graph. A set of rules which co-occur for the finalization of a transformation process is
called a \textbf{transformation recipe}. 
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\newline Figure \ref{method} exemplifies
the approach followed in this work for the production of
both the LOD dataset and a set of knowledge patterns from the FrameNet lexical
database. 
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That approach grounds completely on the transformation method that the
Semion tool implements. 
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The leftmost part of the figure depicts the logical layers of the
original source, i.e. FrameNet, which contains XML data, whose structure is defined by some XSD, which has its own standard meta-model~\cite{Walmsley:04:XSP}. The second leftmost part contains the result of step i): syntactic transformation of FrameNet to pure RDF
triples, whose aim is twofold: (a) extracting data into RDF and (b) flattening
the distinction between the original schema and data in order to provide, via
the refactoring, a customized, task-oriented way to address domain semantics.
\newline Those RDF triples are refactored according to transformation recipes,
either as a LOD dataset (ABox Refactoring, second rightmost column in the
figure) or as Knowledge Patterns (TBox Refactoring, rightmost column).
\begin{figure}[h!]
\centering
	\includegraphics[scale=0.23]{img/methodnew.png}
	\caption{Semion tranformation: key concepts.}\label{method}
\end{figure}

\subsection{FrameNet linked data}
\label{framenet-rdf}
\input{framenet-rdf}
